Decision tree for accurate infection timing in individuals newly diagnosed with HIV-1 infection

Abstract

BackgroundThere is today no gold standard method to accurately define the time passed since infection at HIV diagnosis. Infection timing and incidence measurement is however essential to better monitor the dynamics of local epidemics and the effect of prevention initiatives.MethodsThree methods for infection timing were evaluated using 237 serial samples from documented seroconversions and 566 cross sectional samples from newly diagnosed patients: identification of antibodies against the HIV p31 protein in INNO-LIA, SediaTM BED CEIA and SediaTM LAg-Avidity EIA. A multi-assay decision tree for infection timing was developed.ResultsClear differences in recency window between BED CEIA, LAg-Avidity EIA and p31 antibody presence were observed with a switch from recent to long term infection a median of 169.5, 108.0 and 64.5 days after collection of the pre-seroconversion sample respectively. BED showed high reliability for identification of long term infections while LAg-Avidity is highly accurate for identification of recent infections. Using BED as initial assay to identify the long term infections and LAg-Avidity as a confirmatory assay for those classified as recent infection by BED, explores the strengths of both while reduces the workload. The short recency window of p31 antibodies allows to discriminate very early from early infections based on this marker. BED recent infection results not confirmed by LAg-Avidity are considered to reflect a period more distant from the infection time. False recency predictions in this group can be minimized by elimination of patients with a CD4 count of less than 100 cells/mm3 or without no p31 antibodies. For 566 cross sectional sample the outcome of the decision tree confirmed the infection timing based on the results of all 3 markers but reduced the overall cost from 13.2 USD to 5.2 USD per sample.ConclusionsA step-wise multi assay decision tree allows accurate timing of the HIV infection at diagnosis at affordable effort and cost and can be an important new tool in studies analyzing the dynamics of local epidemics or the effects of prevention strategies.

Prevalence of sexually transmitted infections among foreigners living in Guangzhou, China: a cross-sectional study (2010–2017)

“…First, this article used the secondary data, so the genotypes of various sexually transmitted diseases pathogens were not clear. Second, HIV cases were too small to perform a multiple linear regression, decision trees, or other statistical methods used for analysis [29]. Third, all foreigners who arrive in Guangzhou will accept a physical examination, but some data are incomplete and we removed these data from our study, which may bias the results.…”

Exploring HIV-1 Transmission Dynamics by Combining Phylogenetic Analysis and Infection Timing

“…Time since infection was estimated for patients for whom sufficient leftover serum or plasma, collected within one month after diagnosis, was available (n = 1033, 87.2%). Infection timing was performed following an algorithm described before, with slight modifications [21]. Patients diagnosed during the pre-seroconversion stage were classified as early diagnosed and patients with a CD4+ T-cell count of 100 or less were classified as late diagnosed.…”

“…Unless a seroconversion is demonstrated however, defining the time between infection and diagnosis is challenging. We used a previously validated algorithm that allows to discriminate between patients diagnosed less than four months after infection (early diagnosed) and patients diagnosed at least four months after infection (late diagnosed) [21]. The algorithm relies on two commercial HIV incidence assays, the Sedia BED HIV-1 EIA and the Sedia HIV-1 LAg-Avidity EIA that respectively assess HIV-1 specific antibody concentration and HIV-1 specific antibody affinity.…”

Earlier Initiation of Antiretroviral Treatment Coincides With an Initial Control of the HIV-1 Sub-Subtype F1 Outbreak Among Men-Having-Sex-With-Men in Flanders, Belgium

“…Patient socio-demographics and detailed sample information were collected from mandatory reporting and medical records at the participating ARL in Belgium (gender, year of birth, HIV-1 diagnosis date, infection stage at diagnosis, country of birth, probable country of infection, sexual risk factor for HIV-1 acquisition, viral load, CD4 and CD8 count associated to the HIV-1 pol sequence, and therapy initiation date). Infection stage at diagnosis was defined based upon laboratory data: HIV-1 confirmation results with a positive p24 test combined with a negative or undetermined blot or INNO-LIA HIV I/II (Fujirebio Europe, Ghent, Belgium) were classified as acute infections while results with a positive blot lacking a p31 band as recent infections (Verhofstede et al, 2017). In all other instances, at least when records were available, infections were classified as chronic.…”